Exploring the Association between Personality and Attitudes Towards Ageing in UK and Canadian Older Adults’; Use of a Novel Behavioural Artificial Intelligence Solution
Bibliographic record
Abstract
Perceptions of ageing is an important psychosocial factor that influences health, wellbeing as well as engagement in health- promoting behaviours as people age. This study used a novel Behavioural Artificial Intelligence (AI) solution to examine the association between personality and attitudes towards ageing, and to explore how perceptions of ageing impact health promoting behaviours in UK and Canadian adults. Using a cross-sectional online survey methodology, 1011 UK and 1023 Canadian adults aged 65 years and older were recruited. Participants completed the attitudes towards ageing questionnaire short form and responded to 5 open-ended questions relating to their perceptions of ageing. Natural language computed in the open-ended responses was analysed using Scaled Insights Behavioural AI solution to examine personality attributes associated with attitudes towards ageing. Thematic analysis was also conducted to explore themes that emerged in the participants responses to the open-ended questions. Two personality clusters were identified that were associated with attitudes towards ageing.: Optimistic Ageing and Pessimistic Ageing. The Optimistic Ageing personality cluster had significantly more positive attitudes towards ageing; i.e., towards physical change, and psychosocial loss. Six significant themes emerged relating to participants perceptions of ageing, between those in the Optimistic Ageing and Pessimistic Ageing personality clusters; 1) attitudes towards physical activity and health, 2) mental and emotional health, 3) social connections and relationships, 4) independence and autonomy, 5) attitudes towards ageing and mortality, and 6) experiences of ageism. The findings highlight the important role of personality in attitudes towards ageing and the need for psychologists and other interventionists to consider personality in addressing negative attitudes towards ageing as well as actions to promote healthy behaviour. The novel Behavioural AI solution employed in this study, highlights the potential value of understanding personality both in predicting attitudes towards ageing but also in designing interventions and communications that are more personalised and target older adults based on their personality attributes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".